Integrating genetic data from families with highly penetrant forms of disease together with genetic data from outbred populations represents a promising strategy to uncover the complete frequency spectrum of risk alleles for complex traits such as rheumatoid arthritis (RA). Here, we demonstrate that rare, low-frequency and common alleles at one gene locus, phospholipase B1 (PLB1), might contribute to risk of RA in a 4-generation consanguineous pedigree (Middle Eastern ancestry) and also in unrelated individuals from the general population (European ancestry). Through identity-by-descent (IBD) mapping and whole-exome sequencing, we identified a non-synonymous c.2263G>C (p.G755R) mutation at the PLB1 gene on 2q23, which significantly co-segregated with RA in family members with a dominant mode of inheritance (P = 0.009). We further evaluated PLB1 variants and risk of RA using a GWAS meta-analysis of 8,875 RA cases and 29,367 controls of European ancestry. We identified significant contributions of two independent non-coding variants near PLB1 with risk of RA (rs116018341 [MAF = 0.042] and rs116541814 [MAF = 0.021], combined P = 3.2×10−6). Finally, we performed deep exon sequencing of PLB1 in 1,088 RA cases and 1,088 controls (European ancestry), and identified suggestive dispersion of rare protein-coding variant frequencies between cases and controls (P = 0.049 for C-alpha test and P = 0.055 for SKAT). Together, these data suggest that PLB1 is a candidate risk gene for RA. Future studies to characterize the full spectrum of genetic risk in the PLB1 genetic locus are warranted.
The prevalence of variant alleles among single nucleotide polymorphisms (SNPs) is not well known for many minority populations. These population allele frequencies (PAFs) are necessary to guide genetic epidemiology studies and to understand the population specific contribution of these variants to disease risk. Large differences in PAF among certain functional groups of genes could also indicate possible selection pressure or founder effects of interest. The 50K SNP, custom genotyping microarray (CARe) was developed, focusing on about 2,000 candidate genes and pathways with demonstrated pathophysiologic influence on cardiovascular disease (CVD).
The CARe microarray was used to genotype 216 unaffected controls in a study of pre-eclampsia among a Northern Plains, American Indian tribe. The allelic prevalences of 34,240 SNPs suitable for analysis, were determined and compared with corresponding HapMap prevalences for the Caucasian population. Further analysis was conducted to compare the frequency of statistically different prevalences among functionally related SNPs, as determined by the DAVID Bioinformatics Resource.
Of the SNPs with PAFs in both datasets, 9.8%,37.2% and 47.1% showed allele frequencies among the American Indian population greater than, less than and either greater or less than (respectively) the HapMap Caucasian population. The 2,547 genes were divided into 53 functional groups using the highest stringency criteria. While none of these groups reached the Bonferroni corrected p value of 0.00094, there were 7 of these 53 groups with significantly more or less differing PAFs, each with a probability of less than 0.05 and an overall probability of 0.0046.
In comparison to the HapMap Caucasian population, there are substantial differences in the prevalence among an American Indian community of SNPs related to CVD. Certain functional groups of genes and related SNPs show possible evidence of selection pressure or founder effects.
Immobilisation in the intensive care unit (ICU) leads to muscle weakness and is associated with increased costs and long-term functional disability. Previous studies showed early mobilisation of medical ICU patients improves clinical outcomes. The Surgical ICU Optimal Mobilisation Score (SOMS) trial aims to test whether a budget-neutral intervention to facilitate goal-directed early mobilisation in the surgical ICU improves participant mobilisation and associated clinical outcomes.
Methods and analysis
The SOMS trial is an international, multicentre, randomised clinical study being conducted in the USA and Europe. We are targeting 200 patients. The primary outcome is average daily SOMS level and key secondary outcomes are ICU length of stay until discharge readiness and ‘mini’ modified Functional Independence Measure (mmFIM) at hospital discharge. Additional secondary outcomes include quality of life assessed at 3 months after hospital discharge and global muscle strength at ICU discharge. Exploratory outcomes will include: ventilator-free days, ICU and hospital length of stay and 3-month mortality. We will explore genetic influences on the effectiveness of early mobilisation and centre-specific effects of early mobilisation on outcomes.
Ethics and dissemination
Following Institutional Review Board (IRB) approval in three institutions, we started study recruitment and plan to expand to additional centres in Germany and Italy. Safety monitoring will be the domain of the Data and Safety Monitoring Board (DSMB). The SOMS trial will also explore the feasibility of a transcontinental study on early mobilisation in the surgical ICU.
The results of this study, along with those of ancillary studies, will be made available in the form of manuscripts and presentations at national and international meetings.
This study has been registered at clinicaltrials.gov (NCT01363102).
REHABILITATION MEDICINE; RESPIRATORY MEDICINE (see Thoracic Medicine)
The etiology of pre-eclampsia (PE) is unknown; but it is accepted that normal pregnancy represents a distinctive challenge to the maternal immune system. C-reactive protein is a prominent component of the innate immune system; and we previously reported an association between PE and the CRP polymorphism, rs1205. Our aim was to explore the effects of additional CRP variants. The IBC (Cardiochip) genotyping microarray focuses on candidate genes and pathways related to the pathophysiology of cardiovascular disease.
This study recruited 140 cases of PE and 270 matched controls, of which 95 cases met criteria as severe PE, from an American Indian community. IBC array genotypes from 10 suitable CRP SNPs were analyzed. A replication sample of 178 cases and 427 controls of European ancestry was also genotyped.
A nominally significant difference (p value <0.05) was seen in the distribution of discordant matched pairs for rs3093068; and Bonferroni corrected differences (P<0.005) were seen for rs876538, rs2794521, and rs3091244. Univariate conditional logistic regression odds ratios (OR) were nominally significant for rs3093068 and rs876538 models only. Multivariate logistic models with adjustment for mother's age, nulliparity and BMI attenuated the effect (OR 1.58, P = 0.066, 95% CI 0.97–2.58) for rs876538 and (OR 2.59, P = 0.050, 95% CI 1.00–6.68) for rs3093068. An additive risk score of the above two risk genotypes shows a multivariate adjusted OR of 2.04 (P = 0.013, 95% CI 1.16–3.56). The replication sample also demonstrated significant association between PE and the rs876538 allele (OR = 1.55, P = 0.01, 95% CI 2.16–1.10). We also show putative functionality for the rs876538 and rs3093068 CRP variants.
The CRP variants, rs876538 and rs3093068, previously associated with other cardiovascular disease phenotypes, show suggestive association with PE in this American Indian population, further supporting a possible role for CRP in PE.
Genome wide association studies revealed that variation in the Melatonin Receptor 1B gene (MTNR1B) is associated with insulin and glucose concentrations. Here we show that the risk genotype of this SNP predicts future type 2 diabetes (T2D) in two large prospective studies. Specifically, the risk genotype was associated with impairment of early insulin response to both oral and intravenous glucose and with faster deterioration of insulin secretion over time. We also show that the Melatonin Receptor 1B mRNA is expressed in human islets, and immunocytochemistry confirms that it is primarily localized in β-cells in islets. Non-diabetic individuals carrying the risk allele and patients with T2D showed increased expression of the receptor in islets. Insulin release from clonal β-cells in response to glucose was inhibited in the presence of melatonin. These data suggest that the circulating hormone melatonin, which is predominantly released from the pineal gland in the brain, is involved in the pathogenesis of T2D. Given the increased expression of Melatonin Receptor 1B in individuals at risk of T2D, the pathogenic effects are likely exerted via a direct inhibitory effect on β-cells. In view of these results, blocking the melatonin ligand-receptor system could be a therapeutic avenue in T2D.
We hypothesized that BMI and DNA variants would predict age at menarche in PCOS. Subjects: Subjects with PCOS defined by the NIH criteria (n=522) and controls with regular menstrual cycles and no hyperandrogenism (n=472); aged 18 to 45 years were studied.
Age at menarche was compared between PCOS cases and controls, and examined as a function of multiple parameters.
There was a strong inverse relationship between BMI and age at menarche in PCOS (r=−0.32; p=5×10−11). The chromosome 6 rs7759938-T variant was associated with earlier age at menarche in women with PCOS (12.60±0.09 vs. 13.41±0.23 years; genotype TT vs. CC; p=0.006). Age at menarche was predicted by PCOS status (β=0.512; p<0.001), reported weight group at 10 to 14 years (β= −0.432; p<0.001), current BMI (β= −0.0202; p=0.01) and genotype (β=0.169; p=0.02).
Age at menarche in women with PCOS is influenced by BMI and genetic variants near LIN28B.
BMI; birthweight; breastfeeding; puberty
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.
The population genetic structure of Native Hawaiians has yet to be comprehensively studied, and the ancestral origins of Polynesians remain in question. In this study, we utilized high-resolution genome-wide SNP data and mitochondrial genomes of 148 and 160 Native Hawaiians, respectively, to characterize their population structure of the nuclear and mitochondrial genomes, ancestral origins, and population expansion. Native Hawaiians, who self-reported full Native Hawaiian heritage, demonstrated 78% Native Hawaiian, 11.5% European, and 7.8% Asian ancestry with 99% belonging to the B4 mitochondrial haplogroup. The estimated proportions of Native Hawaiian ancestry for those who reported mixed ancestry (i.e. 75% and 50% Native Hawaiian heritage) were found to be consistent with their self-reported heritage. A significant proportion of Melanesian ancestry (mean = 32%) was estimated in 100% self-reported Native Hawaiians in an ADMIXTURE analysis of Asian, Melanesian, and Native Hawaiian populations of K = 2, where K denotes the number of ancestral populations. This notable proportion of Melanesian admixture supports the “Slow-Boat” model of migration of ancestral Polynesian populations from East Asia to the Pacific Islands. In addition, approximately 1,300 years ago a single, strong expansion of the Native Hawaiian population was estimated. By providing important insight into the underlying population structure of Native Hawaiians, this study lays the foundation for future genetic association studies of this U.S. minority population.
The incretin hormone GIP (glucose-dependent insulinotropic polypeptide) promotes pancreatic β-cell function by potentiating insulin secretion and β-cell proliferation. Recently, a combined analysis of several genome-wide association studies (Meta-analysis of Glucose and Insulin-Related Traits Consortium [MAGIC]) showed association to postprandial insulin at the GIP receptor (GIPR) locus. Here we explored mechanisms that could explain the protective effects of GIP on islet function.
RESEARCH DESIGN AND METHODS
Associations of GIPR rs10423928 with metabolic and anthropometric phenotypes in both nondiabetic (N = 53,730) and type 2 diabetic individuals (N = 2,731) were explored by combining data from 11 studies. Insulin secretion was measured both in vivo in nondiabetic subjects and in vitro in islets from cadaver donors. Insulin secretion was also measured in response to exogenous GIP. The in vitro measurements included protein and gene expression as well as measurements of β-cell viability and proliferation.
The A allele of GIPR rs10423928 was associated with impaired glucose- and GIP-stimulated insulin secretion and a decrease in BMI, lean body mass, and waist circumference. The decrease in BMI almost completely neutralized the effect of impaired insulin secretion on risk of type 2 diabetes. Expression of GIPR mRNA was decreased in human islets from carriers of the A allele or patients with type 2 diabetes. GIP stimulated osteopontin (OPN) mRNA and protein expression. OPN expression was lower in carriers of the A allele. Both GIP and OPN prevented cytokine-induced reduction in cell viability (apoptosis). In addition, OPN stimulated cell proliferation in insulin-secreting cells.
These findings support β-cell proliferative and antiapoptotic roles for GIP in addition to its action as an incretin hormone. Identification of a link between GIP and OPN may shed new light on the role of GIP in preservation of functional β-cell mass in humans.
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Recent genetic studies have identified hundreds of regions of the human genome that contribute to risk for type 2 diabetes, coronary artery disease and myocardial infarction, and to related quantitative traits such as body mass index, glucose and insulin levels, blood lipid levels, and blood pressure. These results motivate two central questions: (1) can further genetic investigation identify additional associated regions?; and (2) can more detailed genetic investigation help us identify the causal variants (or variants more strongly correlated with the causal variants) in the regions identified so far? Addressing these questions requires assaying many genetic variants in DNA samples from thousands of individuals, which is expensive and timeconsuming when done a few SNPs at a time. To facilitate these investigations, we designed the “Metabochip,” a custom genotyping array that assays variation in nearly 200,000 sites in the human genome. Here we describe the Metabochip, evaluate its performance in assaying human genetic variation, and describe solutions to methodological challenges commonly encountered in its analysis.
Over 30 loci have been associated with risk of type 2 diabetes at genome-wide statistical significance. Genetic risk scores (GRSs) developed from these loci predict diabetes in the general population. We tested if a GRS based on an updated list of 34 type 2 diabetes–associated loci predicted progression to diabetes or regression toward normal glucose regulation (NGR) in the Diabetes Prevention Program (DPP).
RESEARCH DESIGN AND METHODS
We genotyped 34 type 2 diabetes–associated variants in 2,843 DPP participants at high risk of type 2 diabetes from five ethnic groups representative of the U.S. population, who had been randomized to placebo, metformin, or lifestyle intervention. We built a GRS by weighting each risk allele by its reported effect size on type 2 diabetes risk and summing these values. We tested its ability to predict diabetes incidence or regression to NGR in models adjusted for age, sex, ethnicity, waist circumference, and treatment assignment.
In multivariate-adjusted models, the GRS was significantly associated with increased risk of progression to diabetes (hazard ratio [HR] = 1.02 per risk allele [95% CI 1.00–1.05]; P = 0.03) and a lower probability of regression to NGR (HR = 0.95 per risk allele [95% CI 0.93–0.98]; P < 0.0001). At baseline, a higher GRS was associated with a lower insulinogenic index (P < 0.001), confirming an impairment in β-cell function. We detected no significant interaction between GRS and treatment, but the lifestyle intervention was effective in the highest quartile of GRS (P < 0.0001).
A high GRS is associated with increased risk of developing diabetes and lower probability of returning to NGR in high-risk individuals, but a lifestyle intervention attenuates this risk.
Rare mitochondrial mutations cause neurologic disease including ischemic stroke and MRI white matter changes. We investigated whether common mitochondrial genetic variants influence risk of sporadic ischemic stroke and, in patients with stroke, the volume of white matter hyperintensity (WMHV).
In this multicenter, mitochondrial genome-wide association study (GWAS), 2284 ischemic stroke cases and 1728 controls from the International Stroke Genetics Consortium were genotyped for 64 mitochondrial single nucleotide polymorphisms (SNPs). Imputation resulted in 144 SNPs, which were tested in each cohort and in meta-analysis for ischemic stroke association. A genetic score of all mitochondrial variants was also tested in association with ischemic stroke.
No individual SNP reached adjusted significance in meta-analysis. A genetic score comprised of the summation of contributions from individual variants across the mitochondrial genome showed association with ischemic stroke in meta-analysis (OR = 1.13, p < 0.0001) with minimal heterogeneity (I2 = 0.00). This ischemic stroke score was robust to permutation, and was also associated with WMHV in 792 nested case individuals with ischemic stroke (p = 0.037).
In this mitochondrial GWAS of ischemic stroke, a genetic score comprised of the sum of all common variants in the mitochondrial genome showed association with ischemic stroke. In an independent analysis of a related trait, this same score correlated with WMHV in stroke cases. Despite this aggregate association, no individual variant reached significance. Substantially larger studies will be required to identify precise sequence variants influencing cerebrovascular disease.
mitochondria; stroke; genes; GWAS; white matter
Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.
RESEARCH DESIGN AND METHODS
We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.
We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10−4). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.
We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.
Human height is a classic, highly heritable quantitative trait. To begin to identify genetic variants influencing height, we examined genome-wide association data from 4,921 individuals. Common variants in the HMGA2 oncogene, exemplified by rs1042725, were associated with height (P = 4 × 10−8). HMGA2 is also a strong biological candidate for height, as rare, severe mutations in this gene alter body size in mice and humans, so we tested rs1042725 in additional samples. We confirmed the association in 19,064 adults from four further studies (P = 3 × 10−11, overall P = 4 × 10−16, including the genome-wide association data). We also observed the association in children (P = 1 × 10−6, N = 6,827) and a tall/short case-control study (P = 4 × 10−6, N = 3,207). We estimate that rs1042725 explains ~0.3% of population variation in height (~0.4 cm increased adult height per C allele). There are few examples of common genetic variants reproducibly associated with human quantitative traits; these results represent, to our knowledge, the first consistently replicated association with adult and childhood height.
Circulating glucose levels are tightly regulated. To identify novel glycemic loci, we performed meta-analyses of 21 genome-wide associations studies informative for fasting glucose (FG), fasting insulin (FI) and indices of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 non-diabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with FG/HOMA-B and two associated with FI/HOMA-IR. These include nine new FG loci (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and FAM148B) and one influencing FI/HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB/TMEM195 with type 2 diabetes (T2D). Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify T2D risk loci, as well as loci that elevate FG modestly, but do not cause overt diabetes.
Aims and Hypothesis
Circulating β-carotene levels are inversely associated with type 2 diabetes risk, but the causal direction of this association is not certain. In this study we used a Mendelian Randomization approach to provide evidence for or against the causal role of the anti-oxidant vitamin β-carotene in type 2 diabetes.
We used a common polymorphism (rs6564851) near the β-carotene 15,15'-Monooxygenase 1 (BCMO1) gene that is strongly associated with circulating β-carotene levels (P = 2×10−24) - each G allele is associated with a 0.27 standard deviation increase in levels. We used data from the InCHIANTI study and the ULSAM study to estimate the association between β-carotene levels and type 2 diabetes. We next used a triangulation approach to estimate the expected effect of rs6564851 on type 2 diabetes risk, and compared this to the observed effect using data from 4549 type 2 diabetes cases and 5579 controls from the DIAGRAM consortium.
A 0.27 standard deviation increase in β-carotene levels is associated with an odds ratio of 0.90 (0.86–0.95) for type 2 diabetes in the InCHIANTI study. This association is similar to that of the ULSAM study, OR (0.90 (0.84–0.97)). In contrast there was no association between rs6564851 and type 2 diabetes (OR 0.98 (0.93–1.04, P = 0.58), and this effect size was smaller than that expected given the known associations between rs6564851 and β-carotene levels and the associations between β-carotene levels and type 2 diabetes.
Our Mendelian Randomization studies are in keeping with randomized controlled trials that suggest β-carotene is not causally protective against type 2 diabetes.
type 2 diabetes; β-carotene; mendelian randomization
Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958–30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, β (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 × 10−15). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 × 10−17; ratio of insulin to glucose area under the curve, P = 1.3 × 10−16) and diminished incretin effect (n = 804; P = 4.3 × 10−4). We also identified variants at ADCY5 (rs2877716, P = 4.2 × 10−16), VPS13C (rs17271305, P = 4.1 × 10−8), GCKR (rs1260326, P = 7.1 × 10−11) and TCF7L2 (rs7903146, P = 4.2 × 10−10) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09–1.15, P = 4.8 × 10−18).
To identify novel genetic loci associated with fasting glucose concentrations, we examined the leading association signals in 10 genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding the melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G-allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95%CI 0.06–0.08) mmol/L in fasting glucose levels (P=3.2×10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P=1.1×10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05–1.12), per G allele P=3.3×10−7) in a meta-analysis of thirteen case-control studies totalling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P=1.1×10−57) and GCK (rs4607517, P=1.0×10−25) loci.
The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P≤5×10−8). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P≤0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2×10−19 for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9×10−8, n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5×10−6, n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2×10−3, n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk.
Through a meta-analysis of genome-wide association studies of 14,733 individuals, we identified common base-pair variants in the genome which influence circulating adiponectin levels. Since adiponectin is an adipocyte-derived circulating protein which has been inversely associated with risk of obesity-related diseases such as type 2 diabetes (T2D) and coronary heart disease (CHD), we next sought to understand if the identified variants influencing adiponectin levels also influence risk of T2D, CHD, and several metabolic traits. In addition to confirming that variation at the ADIPOQ locus influences adiponectin levels, our analyses point to a variant in the ARL15 (ADP-ribosylation factor-like 15) locus which decreases adiponectin levels and increases risk of CHD and T2D. Further, this same variant was associated with increased fasting insulin levels and glycated hemoglobin. While the function of ARL15 is not known, we provide insight into the tissue specificity of ARL15 expression. These results thus provide novel insights into the physiology of the adiponectin pathway and obesity-related diseases.
OBJECTIVE—Using the genome-wide association approach, we recently identified the glucokinase regulatory protein gene (GCKR, rs780094) region as a novel quantitative trait locus for plasma triglyceride concentration in Europeans. Here, we sought to study the association of GCKR variants with metabolic phenotypes, including measures of glucose homeostasis, to evaluate the GCKR locus in samples of non-European ancestry and to fine- map across the associated genomic interval.
RESEARCH DESIGN AND METHODS—We performed association studies in 12 independent cohorts comprising >45,000 individuals representing several ancestral groups (whites from Northern and Southern Europe, whites from the U.S., African Americans from the U.S., Hispanics of Caribbean origin, and Chinese, Malays, and Asian Indians from Singapore). We conducted genetic fine-mapping across the ∼417-kb region of linkage disequilibrium spanning GCKR and 16 other genes on chromosome 2p23 by imputing untyped HapMap single nucleotide polymorphisms (SNPs) and genotyping 104 SNPs across the associated genomic interval.
RESULTS—We provide comprehensive evidence that GCKR rs780094 is associated with opposite effects on fasting plasma triglyceride (Pmeta = 3 × 10−56) and glucose (Pmeta = 1 × 10−13) concentrations. In addition, we confirmed recent reports that the same SNP is associated with C-reactive protein (CRP) level (P = 5 × 10−5). Both fine-mapping approaches revealed a common missense GCKR variant (rs1260326, Pro446Leu, 34% frequency, r2 = 0.93 with rs780094) as the strongest association signal in the region.
CONCLUSIONS—These findings point to a molecular mechanism in humans by which higher triglycerides and CRP can be coupled with lower plasma glucose concentrations and position GCKR in central pathways regulating both hepatic triglyceride and glucose metabolism.
OBJECTIVE— Genome-wide association scans (GWASs) have identified novel diabetes-associated genes. We evaluated how these variants impact diabetes incidence, quantitative glycemic traits, and response to preventive interventions in 3,548 subjects at high risk of type 2 diabetes enrolled in the Diabetes Prevention Program (DPP), which examined the effects of lifestyle intervention, metformin, and troglitazone versus placebo.
RESEARCH DESIGN AND METHODS— We genotyped selected single nucleotide polymorphisms (SNPs) in or near diabetes-associated loci, including EXT2, CDKAL1, CDKN2A/B, IGF2BP2, HHEX, LOC387761, and SLC30A8 in DPP participants and performed Cox regression analyses using genotype, intervention, and their interactions as predictors of diabetes incidence. We evaluated their effect on insulin resistance and secretion at 1 year.
RESULTS— None of the selected SNPs were associated with increased diabetes incidence in this population. After adjustments for ethnicity, baseline insulin secretion was lower in subjects with the risk genotype at HHEX rs1111875 (P = 0.01); there were no significant differences in baseline insulin sensitivity. Both at baseline and at 1 year, subjects with the risk genotype at LOC387761 had paradoxically increased insulin secretion; adjustment for self-reported ethnicity abolished these differences. In ethnicity-adjusted analyses, we noted a nominal differential improvement in β-cell function for carriers of the protective genotype at CDKN2A/B after 1 year of troglitazone treatment (P = 0.01) and possibly lifestyle modification (P = 0.05).
CONCLUSIONS— We were unable to replicate the GWAS findings regarding diabetes risk in the DPP. We did observe genotype associations with differences in baseline insulin secretion at the HHEX locus and a possible pharmacogenetic interaction at CDKNA2/B.
Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been identified as the strongest genetic risk factors for type 2 diabetes (T2D). However, the mechanisms by which these non-coding variants increase risk for T2D are not well-established. We used 13 expression assays to survey mRNA expression of multiple TCF7L2 splicing forms in up to 380 samples from eight types of human tissue (pancreas, pancreatic islets, colon, liver, monocytes, skeletal muscle, subcutaneous adipose tissue and lymphoblastoid cell lines) and observed a tissue-specific pattern of alternative splicing. We tested whether the expression of TCF7L2 splicing forms was associated with single nucleotide polymorphisms (SNPs), rs7903146 and rs12255372, located within introns 3 and 4 of the gene and most strongly associated with T2D. Expression of two splicing forms was lower in pancreatic islets with increasing counts of T2D-associated alleles of the SNPs: a ubiquitous splicing form (P = 0.018 for rs7903146 and P = 0.020 for rs12255372) and a splicing form found in pancreatic islets, pancreas and colon but not in other tissues tested here (P = 0.009 for rs12255372 and P = 0.053 for rs7903146). Expression of this form in glucose-stimulated pancreatic islets correlated with expression of proinsulin (r2 = 0.84–0.90, P < 0.00063). In summary, we identified a tissue-specific pattern of alternative splicing of TCF7L2. After adjustment for multiple tests, no association between expression of TCF7L2 in eight types of human tissue samples and T2D-associated genetic variants remained significant. Alternative splicing of TCF7L2 in pancreatic islets warrants future studies. GenBank Accession Numbers: FJ010164–FJ010174.
Genome-wide association (GWA) studies have identified multiple new genomic loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1-11. Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to discover loci at which common alleles have modest effects, we performed meta-analysis of three T2D GWA scans encompassing 10,128 individuals of European-descent and ~2.2 million SNPs (directly genotyped and imputed). Replication testing was performed in an independent sample with an effective sample size of up to 53,975. At least six new loci with robust evidence for association were detected, including the JAZF1 (p=5.0×10−14), CDC123/CAMK1D (p=1.2×10−10), TSPAN8/LGR5 (p=1.1×10−9), THADA (p=1.1×10−9), ADAMTS9 (p=1.2×10−8), and NOTCH2 (p=4.1×10−8) gene regions. The large number of loci with relatively small effects indicates the value of large discovery and follow-up samples in identifying additional clues about the inherited basis of T2D.
Lymphoblastoid cell lines (LCLs), originally collected as renewable sources of DNA, are now being used as a model system to study genotype–phenotype relationships in human cells, including searches for QTLs influencing levels of individual mRNAs and responses to drugs and radiation. In the course of attempting to map genes for drug response using 269 LCLs from the International HapMap Project, we evaluated the extent to which biological noise and non-genetic confounders contribute to trait variability in LCLs. While drug responses could be technically well measured on a given day, we observed significant day-to-day variability and substantial correlation to non-genetic confounders, such as baseline growth rates and metabolic state in culture. After correcting for these confounders, we were unable to detect any QTLs with genome-wide significance for drug response. A much higher proportion of variance in mRNA levels may be attributed to non-genetic factors (intra-individual variance—i.e., biological noise, levels of the EBV virus used to transform the cells, ATP levels) than to detectable eQTLs. Finally, in an attempt to improve power, we focused analysis on those genes that had both detectable eQTLs and correlation to drug response; we were unable to detect evidence that eQTL SNPs are convincingly associated with drug response in the model. While LCLs are a promising model for pharmacogenetic experiments, biological noise and in vitro artifacts may reduce power and have the potential to create spurious association due to confounding.
The use of lymphoblastoid cell lines (LCLs) has evolved from a renewable source of DNA to an in vitro model system to study the genetics of gene expression, drug response, and other traits in a controlled laboratory setting. While convincing relationships between SNPs and mRNA levels (eQTLs) have been described, the degree to which non-genetic variables also influence phenotypes in LCLs is less well characterized. In the course of attempting to map genes for drug responses in vitro, we evaluated the reproducibility of in vitro traits across replicates, the impact of the EBV virus used to transform B cells into cell lines, and the effect of in vitro culture conditions. We found that responses to at least some drugs and levels of many mRNAs can be technically well measured, but vary both across experiments and with non-genetic confounders such as growth rates, EBV levels, and ATP levels. The influence of such non-genetic factors can both decrease power to detect true relationships between DNA variation and traits and create the potential for non-genetic confounding and spurious associations between DNA variants and traits.